Cargando…
A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098696/ https://www.ncbi.nlm.nih.gov/pubmed/37050625 http://dx.doi.org/10.3390/s23073565 |
_version_ | 1785024872155447296 |
---|---|
author | Campanella, Sara Altaleb, Ayham Belli, Alberto Pierleoni, Paola Palma, Lorenzo |
author_facet | Campanella, Sara Altaleb, Ayham Belli, Alberto Pierleoni, Paola Palma, Lorenzo |
author_sort | Campanella, Sara |
collection | PubMed |
description | In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively). |
format | Online Article Text |
id | pubmed-10098696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100986962023-04-14 A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques Campanella, Sara Altaleb, Ayham Belli, Alberto Pierleoni, Paola Palma, Lorenzo Sensors (Basel) Article In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively). MDPI 2023-03-29 /pmc/articles/PMC10098696/ /pubmed/37050625 http://dx.doi.org/10.3390/s23073565 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Campanella, Sara Altaleb, Ayham Belli, Alberto Pierleoni, Paola Palma, Lorenzo A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title | A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title_full | A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title_fullStr | A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title_full_unstemmed | A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title_short | A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques |
title_sort | method for stress detection using empatica e4 bracelet and machine-learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098696/ https://www.ncbi.nlm.nih.gov/pubmed/37050625 http://dx.doi.org/10.3390/s23073565 |
work_keys_str_mv | AT campanellasara amethodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT altalebayham amethodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT bellialberto amethodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT pierleonipaola amethodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT palmalorenzo amethodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT campanellasara methodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT altalebayham methodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT bellialberto methodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT pierleonipaola methodforstressdetectionusingempaticae4braceletandmachinelearningtechniques AT palmalorenzo methodforstressdetectionusingempaticae4braceletandmachinelearningtechniques |